On the occasion of its 30th anniversary, the journal Electronic Markets offered an opportunity to reflect on research published in recent years in the journal and in Bled eConference proceedings. The present paper continues research from 2012 onward, following the research paper published on the occasion of the 25th anniversary of the Bled eConference. Altogether, titles, keywords and abstracts of 211 papers from Electronic Markets journal and 356 papers from Bled eConference proceedings were used in the research. Automatic content analysis was done using Leximancer software. The results provided a broad overview of concepts and themes covered in both the Electronic Markets journal and the Bled eConference proceedings. Results revealed the following overlapping themes: “information,” “service,” “business,” “online,” “social,” and “systems”. The four most important themes in Electronic Markets (“information,” “service,” “business,” and “online”) are also important and well covered in Bled eConference proceedings. In addition to the results of the analysis, the present paper also discusses future research challenges and the role of the Electronic Markets journal and the Bled eConference in bridging the gap between science and real-life challenges.
COBISS.SI-ID: 8178963
The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the classification model, which was then used for classification of old texts on a corpus of 200,000 items. These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used. 15 human experts (librarians) evaluated the performance of the model on the 150 randomly selected texts. Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable
COBISS.SI-ID: 41547267
Ventilator-associated pneumonia is a hospital-acquired infection of the lungs occurring in mechanically ventilated patients. An active risk management approach can prevent the occurrence of the disease and promote positive organizational changes, subsequently decreasing mortality and hospitalization costs. Using scientific and clinical practice knowledge, a risk evaluation model was developed to identify patients more at risk of developing the disease. For this purpose, a Decision Expert qualitative multi-criteria decision method was used, in which alternatives are evaluated according to predetermined hierarchically arranged criteria. Characteristics of each evaluated alternative are described by the members of an interdisciplinary expert team and are represented by the values of the basic criteria. Values of hierarchically higher aggregated criteria are computed in an upwards fashion according to utility functions, which are defined as simple logical rules. This method is integrated into a software solution, DEXi. The approach is applicable to vastly diverse decision problems and has been successfully used before for health-related decision support. The designed model was tested using actual clinical data. Evaluations of alternatives that most distinctly demonstrated the functionality of the evaluation model were selected and are presented in the results. The evaluation model is intended to assist a holistic evaluation of the risk of developing ventilator-associated pneumonia, by considering patient-related risk factors and the use of preventive measures. The model incorporates nursing-specific data that have hitherto been poorly utilized in preventing ventilator-associated pneumonia and promotes the active engagement of nurses in confronting this interdisciplinary healthcare problem, which has gained more prominence with the onset of COVID-19 disease.
COBISS.SI-ID: 46455043
The world economy and society are in a complex process of transition characterized by a high degree of uncertainty. Therefore, further development and management of the transition will largely depend on the quality of the decisions made and, accordingly, on the decision-making process itself. The main goal of this study is to analyze the reliability of International Energy Security Risk Index as a tool to support the process of energy and economy transition decision making, as closely related and highly interdependent phenomena. The index is composed of 29 aggregated variables (grouped into eight categories), and the research is conducted on a research sample of 25 countries over a period of 36 years. The reliability assessment is performed by using Multiple Regression Analysis. Multicollinearity test, plus Multicollinearity test with Variance Inflation Factors, is used for methodological verification. The test results indicate a high degree of unreliability of the Index, as is concluded based on the observed errors in its methodological settings. These errors primarily relate to a high degree of multicollinearity in all 29 variables, whereby independent variables lose their independence and thus jeopardize reliability of the total Index. Out of the eight groups of variables, the fuel imports group is the only one that does not show big methodological errors. The paper presents a recommendation for the improvement of the observed Index (review of the role of individual variables found to be particularly methodologically indicative), as well as a recommendation for different distribution of weighting coefficients.
COBISS.SI-ID: 22973955
Small and medium-sized enterprises (SMEs) need to keep pace with large enterprises, thus they need to digitally transform. Since they usually lack resources (budget, knowledge, and time) many countries have their support environment to help SMEs in this endeavor. To be able to ensure the right kinds of support, it is crucial to assess the digital maturity of an enterprise. There are many models and assessment tools for digital maturity, however, they are either theoretical models, partial, vendor oriented, or suited for large enterprises. In this paper, we address the problem of assessing digital maturity for SMEs. For this purpose, we developed a multi-attribute model for assessment of the digital maturity of an SME. We followed the design science research approach, where the multi-attribute model is considered as an IT artifact. Within the design cycle, the decision expert (DEX) methodology of a broader multi-attribute decision making methodologies was applied. The developed model was validated by a group of experts and upgraded according to their feedback and finally evaluated on seven real-life cases. Results show that the model can be used in real business situations.
COBISS.SI-ID: 58893315